Artificial heart pumps may be classified into the reciprocating type, the rotary displacement type, the centrifugal type, and the turbo type that operates by rotational flow. Artificial heart pumps of the centrifugal type may be equipped with a casing, a rotor disposed inside the casing, a motor for rotating the rotor, a blood flow channel for introducing and guiding the flow of blood, and an impeller that rotates integrally with the rotor for imparting centrifugal force to the blood flowing in through the blood flow channel formed in the casing.
Centrifugal type artificial hearts conventionally use ball bearings for rotatably supporting the rotor coupled with the impeller. In such configurations, blood flow is liable to stagnate in the vicinity of the ball bearing. An artificial heart pump that is susceptible to such stagnation may pose significant problems. The formation of stagnant blood is known to be a primary cause of blood coagulation. To eliminate this drawback, artificial heart pumps that include rotors suspended in a non-contacting state by magnetic forces have been used. These magnetically levitated heart pumps may constantly maintain the rotor in the proper attitude by regulating the current supplied to the electromagnets so as to control their magnetic field.
Prior efforts at developing a flow estimation method for artificial hearts, in an attempt to properly regulate the current supplied to the electromagnets, have focused on developing a linear equation describing the physics of the system, then using least-squared methods to find a best fit for the coefficients for the equation. These prior efforts, however, do not provide accurate estimates for magnetically levitated heart pumps because any dynamic force across the pump may perturb the position of the levitated rotor, which may change properties of the levitated pump. As a result, benefits may be realized by providing systems and methods for predicting characteristics of an artificial heart using an artificial neural network.